A Multiplicative Iterative Algorithm for Box-Constrained Penalized Likelihood Image Restoration
Publication in refereed journal

香港中文大學研究人員

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其它資訊
摘要Image restoration is a computationally intensive problem as a large number of pixel values have to be determined. Since the pixel values of digital images can attain only a finite number of values (e. g., 8-bit images can have only 256 gray levels), one would like to recover an image within some dynamic range. This leads to the imposition of box constraints on the pixel values. The traditional gradient projection methods for constrained optimization can be used to impose box constraints, but they may suffer from either slow convergence or repeated searching for active sets in each iteration. In this paper, we develop a new box-constrained multiplicative iterative (BCMI) algorithm for box-constrained image restoration. The BCMI algorithm just requires pixelwise updates in each iteration, and there is no need to invert any matrices. We give the convergence proof of this algorithm and apply it to total variation image restoration problems, where the observed blurry images contain Poisson, Gaussian, or salt-and-pepper noises.
著者Chan RH, Ma J
期刊名稱IEEE Transactions on Image Processing
出版年份2012
月份7
日期1
卷號21
期次7
出版社Institute of Electrical and Electronics Engineers (IEEE)
頁次3168 - 3181
國際標準期刊號1057-7149
電子國際標準期刊號1941-0042
語言英式英語
關鍵詞Box constraints; box-constrained multiplicative iterative (BCMI) algorithm; global convergence; image restoration; penalized likelihood (PL) optimization
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Engineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

上次更新時間 2020-18-10 於 00:33